Tracing the Interactions of Modular CMA-ES Configurations Across Problem Landscapes
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909674534600704 |
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| author | Nikolikj, Ana Muñoz, Mario Andrés Tuba, Eva Eftimov, Tome |
| author_facet | Nikolikj, Ana Muñoz, Mario Andrés Tuba, Eva Eftimov, Tome |
| contents | This paper leverages the recently introduced concept of algorithm footprints to investigate the interplay between algorithm configurations and problem characteristics. Performance footprints are calculated for six modular variants of the CMA-ES algorithm (modCMA), evaluated on 24 benchmark problems from the BBOB suite, across two-dimensional settings: 5-dimensional and 30-dimensional. These footprints provide insights into why different configurations of the same algorithm exhibit varying performance and identify the problem features influencing these outcomes. Our analysis uncovers shared behavioral patterns across configurations due to common interactions with problem properties, as well as distinct behaviors on the same problem driven by differing problem features. The results demonstrate the effectiveness of algorithm footprints in enhancing interpretability and guiding configuration choices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_02331 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Tracing the Interactions of Modular CMA-ES Configurations Across Problem Landscapes Nikolikj, Ana Muñoz, Mario Andrés Tuba, Eva Eftimov, Tome Neural and Evolutionary Computing Artificial Intelligence This paper leverages the recently introduced concept of algorithm footprints to investigate the interplay between algorithm configurations and problem characteristics. Performance footprints are calculated for six modular variants of the CMA-ES algorithm (modCMA), evaluated on 24 benchmark problems from the BBOB suite, across two-dimensional settings: 5-dimensional and 30-dimensional. These footprints provide insights into why different configurations of the same algorithm exhibit varying performance and identify the problem features influencing these outcomes. Our analysis uncovers shared behavioral patterns across configurations due to common interactions with problem properties, as well as distinct behaviors on the same problem driven by differing problem features. The results demonstrate the effectiveness of algorithm footprints in enhancing interpretability and guiding configuration choices. |
| title | Tracing the Interactions of Modular CMA-ES Configurations Across Problem Landscapes |
| topic | Neural and Evolutionary Computing Artificial Intelligence |
| url | https://arxiv.org/abs/2507.02331 |